Fractionating autism based on neuroanatomical normative modeling

Abstract

Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6-31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case-control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism.

Fig. 1. Anatomical separability of the clusters.

a Structure coefficients. The highlighted regions indicate the importance of each region for multi-class anatomical classification. The positive values are associated with an increased cortical thickness (yellow) and the negative values are in association with the reduced cortical thickness (blue), relative to the other classes. For the purposes of illustration, the structure coefficients were thresholded at P<0.001 however this should not be a formal statistical test since these were estimated under cross-validation. b The average deviations from normative CT across clusters. Respectively, blue and yellow vertices indicate reduced and increased CT relative to the reference cohort. Maps were rescaled for visualization such that the maximum in each image was = 1.

Fig. 2. Correlation of atypicality index with symptoms.

Colored bars show a correlation with P < 0.05. * indicates significant correlations after FDR correction across clusters and blocks. Note that the y axis is fixed across all panels. Autism Diagnostic Interview-Revised (ADI-R), Autism Diagnostic Observation Schedule (ADOS)-2 calibrated severity scores, Social Responsiveness Scale-2 (SRS-2), Repetitive Behavioral Scale-Revised (RBS-R), Short Sensory Profile(SSP), DSM-5 ADHD rating scale for attention deficit hyperactivity disorder (ADHD) symptoms (inattention and hyper-impulsivity). The ADHD scores are parent-report scores.

Fig. 3. Regional atypicality index associations with symptoms.

Only regions surviving FDR correction (q < 0.05) are shown. Note that ADHD scores are parent-reported scores.